Dataset Information

Prediction of Toddlers Acute Respiratory Infection (ARI) to Become Pneumonia in Martapura Catchment Area, Banjar District, Indonesia.

ABSTRACT: Introduction. The pneumonia pattern in young children may vary across different catchment areas. Therefore, this study aims to analyze the predictive factors for toddlers with an Acute Respiratory Infection (ARI) developing into pneumonia in the catchment area of Banjar Regency, Indonesia. Methods. A case-control design, with 300 respondents, consisting of 106 cases and 194 controls. A questionnaire of interviews with mothers/caregivers of toddlers. Forty-one indicators data were analyzed using multiple logistic regression with backward stepwise regression to arrive at the final model. Results. The predictive factors for toddlers with pneumonia were the child's age (P-value .070), child development (P-value .007), breastfeeding (P-value .051), family income (P-value .026), and location of houses along the river (P-value .025). Conclusion. A prediction index for toddler pneumonia has been compiled, which can be applied to improve the health of lower middle-class toddlers requiring more government attention.

SUBMITTER: Lutpiatina L

PROVIDER: S-EPMC10894530 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature

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Prediction of Toddlers Acute Respiratory Infection (ARI) to Become Pneumonia in Martapura Catchment Area, Banjar District, Indonesia.

Lutpiatina Leka L Sulistyorini Lilis L Yudhastuti Ririh R Notobroto Hari Basuki HB

Global pediatric health 20240223

<i>Introduction.</i> The pneumonia pattern in young children may vary across different catchment areas. Therefore, this study aims to analyze the predictive factors for toddlers with an Acute Respiratory Infection (ARI) developing into pneumonia in the catchment area of Banjar Regency, Indonesia. <i>Methods.</i> A case-control design, with 300 respondents, consisting of 106 cases and 194 controls. A questionnaire of interviews with mothers/caregivers of toddlers. Forty-one indicators data were a ...[more]

PMID: 38405016

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Project description:Healthcare facilities (HF) may identify catchment areas (CA) by selecting criteria that depend on various factors. These refer to hospital activities, geographical definition, patient covariates, and more. The analyses that were traditionally pursued have a limiting factor in the consideration of only static conditions. Instead, some of the CA determinants involve influences occurring at both temporal and spatial scales. The study of CA in the cancer context means choosing between HF, usually divided into general hospitals versus oncological centers (OCs). In the CA context, electronic health records (EHRs) promise to be a valuable source of information, one driving the next-generation patient-driven clinical decision support systems. Among the challenges, digital health requires the re-definition of a role of stochastic modeling to deal with emerging complexities from data heterogeneity. To model CA with cancer EHR, we have chosen a computational framework centered on a logistic model, as a reference, and on a multivariate statistical approach. We also provided a battery of tests for CA assessment. Our results indicate that a more refined CA model's structure yields superior discrimination power between health facilities. The increased significance was also visualized by comparative evaluations with ad hoc geo-localized maps. Notably, a cancer-specific spatial effect can be noticed, especially for breast cancer and through OCs. To mitigate the data distributional influences, bootstrap analysis was performed, and gains in some cancer-specific and spatially concentrated regions were obtained. Finally, when the temporal dynamics are assessed along a 3-year timeframe, negligible differential effects appear between predicted probabilities observed between standard critical values and bootstrapped values. In conclusion, for interpreting CA in terms of both spatial and temporal dynamics, sophisticated models are required. The one here proposed suggests that bootstrap can improve test accuracy. We recommend that evidences from stochastic modeling are merged with visual analytics, as this combination may be exploited by policy-makers in support to quantitative CA assessment.

Project description:BackgroundHousehold food insecurity is a major public health problem in Ethiopia despite the presence of various interventions implemented by the government. However, there is a dearth of evidence regarding the prevalence and responsible factors in Ethiopia, specifically in the South Ari district. This study, therefore, aimed to assess household food insecurity and associated factors in South Ari district, Southern Ethiopia.MethodsA community-based cross-sectional study was employed from March 11 to April 11, 2021, at South Ari district, Southern Ethiopia. A two-stage sampling technique was used to draw a sample of 717 households. Data were checked and entered into Epi-Data V3.2., and exported to SPSS V25.0 for data exploration and analysis. Variables with a p-value <0.25 in bivariable logistic regression were candidates for multivariable logistic regression. Multivariable logistic regression analysis was fitted to determine factors associated with household food insecurity. Hosmer-Lemeshow goodness-of-fit statistic was used to check model fitness and was satisfied. Adjusted odds ratio (AOR) with a 95% confidence interval (CI) was used to determine the strength of association. P-value <0.05 was used to declare statistical significance.ResultThe prevalence of household food insecurity was 44.8% (95% CI: 41.1%, 48.5%). Larger family size (8 and above) (AOR = 1.91, 95% CI: 1.10, 3.30), high dependency ratio (AOR = 2.71, 95% CI: 1.67, 4.40), medium dependency ratio (AOR = 1.72, 95% CI: 1.13, 2.62), poor wealth index (AOR = 2.30, 95% CI: 1.53, 3.46), not using agricultural extension service (AOR = 2.25, 95% CI: 1.57, 3.23), and non-beneficiary of productive safety net program (AOR = 1.71, 95% CI: 1.01, 2.87) were factors significantly associated with household food insecurity.ConclusionsThe findings of this study showed a significant proportion of households were food insecure in South Ari District. Larger family size, high and medium dependency ratio, poor wealth index, not using agricultural extension service, and non-beneficiary of productive safety net program were significant risk factors associated with household food insecurity. Therefore, rigorous work is highly needed to enhance income-generating activities, strengthen agricultural productivity, expand the productive safety net program, and limit population pressure through improved family planning use.

Dataset Information

Prediction of Toddlers Acute Respiratory Infection (ARI) to Become Pneumonia in Martapura Catchment Area, Banjar District, Indonesia.

Publications

Prediction of Toddlers Acute Respiratory Infection (ARI) to Become Pneumonia in Martapura Catchment Area, Banjar District, Indonesia.

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